Determinants and Spatial Factors of Anemia in Women of Reproductive Age in the DRC: A Bayesian Multilevel Ordinal Logistic Regression Model Approach

Background: As a global public health problem, anemia affects more than 400 million women of reproductive age worldwide, mostly in Africa and India. In the DRC, the prevalence of this anemia has decreased slightly from 52.9% in 2007, to 46.4% in 2012 and 42.4% in 2019. However, there is considerable regional variation in its distribution. Methods: Based on the Bayesian Multilevel Spatial Ordinal Logistic Regression Model, we use the DHS-DRC II data to observe whether individual and environmental characteristics of the reproductive-age woman contribute to the development of anemia and the mapping of anemia in terms of residual spatial effects. Results: Age, pregnancy status, body mass index, education level, current breastfeeding, current marital status, contraceptive and insecticide-treated net use, source of drinking water supply, toilet/latrine use, and province of residence were the factors contributing to anemia in women of reproductive age in DRC. With Global Moran's I = -0.00279, p-value ≥ 0.05, the spatial distribution of anemia in women of reproductive age in DRC results from random spatial processes. Thus, the observed spatial pattern is completely random. Conclusion: The Bayesian Multilevel Spatial Ordinal Logistic Regression statistical model is able to adjust for risk and spatial factors of anemia in women of reproductive age in DRC. Keywords: Determinants and spatial factors, Anemia in women of reproductive age, Multilevel and spatial Bayesian ordinal logistic regression model.